{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:37:58Z","timestamp":1777635478691,"version":"3.51.4"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2018,2,21]],"date-time":"2018-02-21T00:00:00Z","timestamp":1519171200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602254"],"award-info":[{"award-number":["61602254"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772281"],"award-info":[{"award-number":["61772281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Social Science Foundation of China","award":["16ZDA054"],"award-info":[{"award-number":["16ZDA054"]}]},{"name":"Jiangsu Provincial 333 Project","award":["BRA2017396"],"award-info":[{"award-number":["BRA2017396"]}]},{"name":"Six Major Talents PeakProject of Jiangsu Province","award":["XYDXXJS-CXTD-005"],"award-info":[{"award-number":["XYDXXJS-CXTD-005"]}]},{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"crossref","award":["(PAPD)"],"award-info":[{"award-number":["(PAPD)"]}],"id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology","award":["(CICAEET)"],"award-info":[{"award-number":["(CICAEET)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1007\/s11042-018-5772-4","type":"journal-article","created":{"date-parts":[[2018,2,23]],"date-time":"2018-02-23T17:16:03Z","timestamp":1519406163000},"page":"29799-29810","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Few-shot learning for short text classification"],"prefix":"10.1007","volume":"77","author":[{"given":"Leiming","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhui","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,2,21]]},"reference":[{"issue":"5","key":"5772_CR1","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1109\/TNNLS.2016.2527796","volume":"28","author":"Bin Gu","year":"2017","unstructured":"Bin G, Sheng VS (2016) A robust regularization path algorithm for \u03bd-support vector classification. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2016.2527796","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"7","key":"5772_CR2","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1109\/TNNLS.2014.2342533","volume":"26","author":"G Bin","year":"2015","unstructured":"Bin G, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403\u20131416","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5772_CR3","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.neunet.2017.07.001","volume":"94","author":"S Blaes","year":"2017","unstructured":"Blaes S, Burwick T (2017) Few-shot learning in deep networks through global prototyping[J]. Neural Netw Off J Int Neural Netw Soc 94:159\u2013172","journal-title":"Neural Netw Off J Int Neural Netw Soc"},{"key":"5772_CR4","doi-asserted-by":"crossref","unstructured":"Chen B, Qi X, Sun X, Shi Y-Q (2017) Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection. J Vis Commun Image Represent","DOI":"10.1016\/j.jvcir.2017.08.011"},{"issue":"2","key":"5772_CR5","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/s10489-016-0768-0","volume":"45","author":"J Cheng","year":"2016","unstructured":"Cheng J, Zhang X, Li P et al (2016) Exploring sentiment parsing of microblogging texts for opinion polling on Chinese public figures. Appl Intell 45(2):429\u2013442","journal-title":"Appl Intell"},{"issue":"11","key":"5772_CR6","doi-asserted-by":"publisher","first-page":"5427","DOI":"10.1109\/TIP.2016.2607421","volume":"25","author":"G Ding","year":"2016","unstructured":"Ding G, Guo Y, Zhou J, Gao Y (2016) Large-scale cross-modality search via collective matrix factorization hashing. IEEE Trans Image Process 25(11):5427\u20135440","journal-title":"IEEE Trans Image Process"},{"key":"5772_CR7","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neucom.2017.01.055","volume":"257","author":"G Ding","year":"2017","unstructured":"Ding G, Zhou J, Guo Y, Lin Z, Zhao S (2017) Large-scale image retrieval with sparse embedded hashing. Neurocomputing 257:24\u201336","journal-title":"Neurocomputing"},{"key":"5772_CR8","doi-asserted-by":"publisher","unstructured":"Fu Z, Huang F, Sun X, Vasilakos AV, Yang C-N (2016) Enabling semantic search based on conceptual graphs over encrypted outsourced data. IEEE Trans Serv Comput. https:\/\/doi.org\/10.1109\/TSC.2016.2622697","DOI":"10.1109\/TSC.2016.2622697"},{"key":"5772_CR9","unstructured":"Guo Y, Ding G, Han J (2017) Robust quantization for general similarity search. IEEE Trans Image Process PP(99):1\u20131"},{"issue":"3","key":"5772_CR10","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/TIP.2017.2652730","volume":"26","author":"Y Guo","year":"2017","unstructured":"Guo Y, Ding G, Liu L, Han J, Shao L (2017) Learning to hash with optimized anchor embedding for scalable retrieval. IEEE Trans Image Process 26(3):1344\u20131354","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"5772_CR11","doi-asserted-by":"publisher","first-page":"3277","DOI":"10.1109\/TIP.2017.2696747","volume":"26","author":"Yuchen Guo","year":"2017","unstructured":"Guo Y, Ding G, Han J et al Zero-shot learning with transferred samples. IEEE Trans Image Process 26(7):3277","journal-title":"IEEE Transactions on Image Processing"},{"key":"5772_CR12","unstructured":"Han J, Cheng G, Li Z et al (2017) A unified metric learning-based framework for co-saliency detection. IEEE Trans Circuits Syst Video Technol PP(99):1\u20131"},{"key":"5772_CR13","unstructured":"Han J, Chen H, Liu N et al (2017) CNNs-based RGB-D saliency detection via cross-view transfer and multiview fusion[J]. IEEE Trans Cybern PP(99):1\u201313"},{"key":"5772_CR14","unstructured":"Hariharan B, Girshick R (2016). Low-shot visual object recognition. arXiv:1606.02819"},{"key":"5772_CR15","doi-asserted-by":"crossref","unstructured":"Hecht T, Gepperth A (2016). Computational advantages of deep prototype-based learning. In: International conference on artificial neural networks, Springer, pp 121\u2013127","DOI":"10.1007\/978-3-319-44781-0_15"},{"key":"5772_CR16","doi-asserted-by":"crossref","unstructured":"Jetley S, Romera-Paredes B, Jayasumana S, Torr P (2015) Prototypical priors: from improving classification to zero-shot learning. arXiv preprint arXiv:1512. 01192","DOI":"10.5244\/C.29.120"},{"key":"5772_CR17","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. arXiv, 1408.5882","DOI":"10.3115\/v1\/D14-1181"},{"key":"5772_CR18","unstructured":"Koch G, Zemel R, Salakhutdinov R (2015) Siamese neural networks for one-shot image recognition. Proceedings of the 32nd international conference on machine learning, Lille, France"},{"key":"5772_CR19","unstructured":"Lake BM, Salakhutdinov R, Tenenbaum JB (2013) One-shot learning by inverting a compositional causal process[J]. Adv Neural Inf Proces Syst 2526\u20132534"},{"key":"5772_CR20","doi-asserted-by":"crossref","unstructured":"Lampert CH, Nickisch H, Harmeling S (2009) Learning to detect unseen object classes by between-class attribute transfer. In: IEEE conference on computer vision and pattern recognition. CVPR 2009 IEEE, pp 951\u2013958","DOI":"10.1109\/CVPR.2009.5206594"},{"issue":"3","key":"5772_CR21","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1109\/TIFS.2014.2381872","volume":"10","author":"J Li","year":"2015","unstructured":"Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507\u2013518","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"1","key":"5772_CR22","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1002\/asi.21662","volume":"63","author":"T Mike","year":"2012","unstructured":"Mike T, Kevan B, Georgios P (2012) Sentiment strength detection for the social web. J Assoc Inf Sci Technol 63(1):163\u2013173","journal-title":"J Assoc Inf Sci Technol"},{"key":"5772_CR23","doi-asserted-by":"crossref","unstructured":"Mikolov T, Karafi\u00e1t M, Burget L et al (2010) Recurrent neural network based language model. 11th Annual Conference of the International Speech Communication Association, Makuhari, Japan, pp 1045\u20131048","DOI":"10.21437\/Interspeech.2010-343"},{"issue":"1","key":"5772_CR24","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s10579-015-9328-1","volume":"50","author":"P Nakov","year":"2016","unstructured":"Nakov P, Rosenthal S, Kiritchenko S et al (2016) Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts. Lang Resour Eval 50(1):35\u201365","journal-title":"Lang Resour Eval"},{"key":"5772_CR25","unstructured":"Ravi S, Larochelle H (2017) Optimization as a Model for Few-Shot Learning. 5th International Conference on Learning Representations(ICLR), Toulon, France. https:\/\/openreview.net\/pdf?id=rJY0-Kcll"},{"key":"5772_CR26","unstructured":"Rezende DJ, Mohamed S, Danihelka I, Gregor K, Wierstra D (2016) One-shot generalization in deep generative models. arXiv preprint arXiv:1603.05106"},{"key":"5772_CR27","unstructured":"Saif H, Fern\u00e1ndez M, He Y et al (2013) Evaluation datasets for twitter sentiment analysis: a survey and a new dataset, the STS-gold. Proceedings of the first international workshop on emotion and sentiment in social and expressive media: approaches and perspectives from AI, A workshop of the XIII International Conference of the Italian Association for Artificial Intelligence, Turin, Italy, pp 9\u201321"},{"key":"5772_CR28","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815\u2013823","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"5772_CR29","unstructured":"Snell J, Swersky K, Zemel RS (2017) Prototypical networks for few-shot learning. arXiv:1703.05175"},{"key":"5772_CR30","unstructured":"Socher R, Lin CC-Y, Ng AY, Manning CD (2011) Parsing natural scenes and natural language with recursive neural networks. Proceedings of the 28th international conference on machine learning, Washington, USA, pp 129\u2013136"},{"key":"5772_CR31","unstructured":"Speriosu M, Upadhyay S, Sudan N et al (2011) Twitter polarity classification with label propagation over lexical links and the follower graph. Proceedings of the EMNLP First workshop on Unsupervised Learning in NLP, Edinburgh, Scotland, pp 53\u201363"},{"key":"5772_CR32","doi-asserted-by":"crossref","unstructured":"Sundermeyer M, Schl\u00fcter R, Ney H (2012) LSTM neural networks for language modeling. 13th annual conference of the international speech communication association, Portland, USA, pp 194\u2013197","DOI":"10.21437\/Interspeech.2012-65"},{"key":"5772_CR33","doi-asserted-by":"crossref","unstructured":"Tang D, Wei F, Qin B (2014) Coooolll: A deep learning system for Twitter sentiment classification. Proceedings of the 8th International Workshop on Semantic Evaluation, Dublin, Ireland, pp 208\u2013212","DOI":"10.3115\/v1\/S14-2033"},{"key":"5772_CR34","unstructured":"Triantafillou E, Zemel RS, Urtasun R Few-shot learning through an information retrieval lens. arXiv:1707.02610"},{"issue":"1","key":"5772_CR35","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1613\/jair.2934","volume":"37","author":"PD Turney","year":"2010","unstructured":"Turney PD, Pantel P (2010) From frequency to meaning: vector space models of semantics. J Artif Intell Res 37(1):141\u2013188","journal-title":"J Artif Intell Res"},{"key":"5772_CR36","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Adv Neural Inf Process Sys 3630\u20133638"},{"key":"5772_CR37","unstructured":"Wang X, Liu Y, Sun C et al (2012) Predicting polarities of tweets by composing word embeddings with long short-term memory. Unabbreviated Name of Conference, Portland, USA, pp 194\u2013197"},{"issue":"22","key":"5772_CR38","doi-asserted-by":"publisher","first-page":"23721","DOI":"10.1007\/s11042-016-4153-0","volume":"76","author":"Jinwei Wang","year":"2016","unstructured":"Wang J, Li T, Shi Y-Q, Lian S, Ye J Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-016-4153-0","journal-title":"Multimedia Tools and Applications"},{"key":"5772_CR39","unstructured":"Weinberger KQ, Blitzer J, Saul LK (2005) Distance metric learning for large margin nearest neighbor classification. In: Advances in neural information processing systems, pp 1473\u20131480"},{"issue":"7","key":"5772_CR40","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.6138\/JIT.2017.18.7.20170410","volume":"18","author":"L Yan","year":"2017","unstructured":"Yan L, Zheng W, Zhang H(H) et al (2017) Learning discriminative sentiment chunk vectors for twitter sentiment analysis. J Inf Technol 18(7):1605\u20131613. https:\/\/doi.org\/10.6138\/JIT.2017.18.7.20170410","journal-title":"J Inf Technol"},{"issue":"6","key":"5772_CR41","doi-asserted-by":"publisher","first-page":"3660","DOI":"10.1109\/TGRS.2016.2523563","volume":"54","author":"X Yao","year":"2016","unstructured":"Yao X, Han J, Cheng G, Qian X, Guo L (2016) Semantic annotation of high-resolution satellite images via weakly supervised learning. IEEE Trans Geosci Remote Sens 54(6):3660\u20133671","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"7","key":"5772_CR42","doi-asserted-by":"publisher","first-page":"3196","DOI":"10.1109\/TIP.2017.2694222","volume":"26","author":"X Yao","year":"2017","unstructured":"Yao X, Han J, Zhang D, Nie F (2017) Revisiting co-saliency detection: a novel approach based on two-stage multi-view spectral rotation co-clustering. IEEE Trans Image Process 26(7):3196\u20133209","journal-title":"IEEE Trans Image Process"},{"key":"5772_CR43","doi-asserted-by":"crossref","unstructured":"Zhang Z, Saligrama V (2015) Zero-shot learning via semantic similarity embedding. In: Proceedings of the IEEE international conference on computer vision, pp 4166\u20134174","DOI":"10.1109\/ICCV.2015.474"},{"issue":"2","key":"5772_CR44","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11263-016-0907-4","volume":"20","author":"D Zhang","year":"2016","unstructured":"Zhang D, Han J, Li C, Wang J, Li X (2016) Detection of co-salient objects by looking deep and wide. Int J Comput Vis 20(2):215\u2013232","journal-title":"Int J Comput Vis"},{"issue":"4","key":"5772_CR45","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1109\/TIP.2017.2658957","volume":"26","author":"D Zhang","year":"2017","unstructured":"Zhang D, Han J, Jiang L, Ye S, Chang X (2017) Revealing event saliency in unconstrained video collection. IEEE Trans Image Process 26(4):1746\u20131758","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"5772_CR46","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1109\/TPAMI.2016.2567393","volume":"39","author":"D Zhang","year":"2017","unstructured":"Zhang D, Meng D, Han J (2017) Co-saliency detection via a self-paced multiple-instance learning framework. IEEE Trans Pattern Anal Mach Intell 39(5):865\u2013878","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"5772_CR47","doi-asserted-by":"publisher","first-page":"2410","DOI":"10.1109\/TAP.2016.2550058","volume":"64","author":"Y Zhao","year":"2016","unstructured":"Zhao Y, Ding DZ, Chen RS (2016) A discontinuous Galerkin time domain integral equation method for electromagnetic scattering from PEC objects. IEEE Trans Antennas Propag 64(6):2410\u20132417","journal-title":"IEEE Trans Antennas Propag"},{"key":"5772_CR48","doi-asserted-by":"publisher","unstructured":"Zheng Y, Jeon B, Sun L, Zhang J, Zhang H (2017) Student's t-Hidden Markov Model for Unsupervised Learning Using Localized Feature Selection. IEEE Trans Circuits Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2017.2724940","DOI":"10.1109\/TCSVT.2017.2724940"},{"issue":"6","key":"5772_CR49","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1587\/transinf.2015EDP7341","volume":"E99-D","author":"Z Zhou","year":"2016","unstructured":"Zhou Z, Yang C-N, Chen B, Sun X, Liu Q, Wu QMJ (2016) Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Trans Inf Syst E99-D(6):1531\u20131540","journal-title":"IEICE Trans Inf Syst"},{"issue":"1","key":"5772_CR50","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/TIFS.2016.2601065","volume":"12","author":"Z Zhou","year":"2017","unstructured":"Zhou Z, Wang Y, Jonathan Wu QM, Yang C-N, Sun X (2017) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur 12(1):48\u201363","journal-title":"IEEE Trans Inf Forensics Secur"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-018-5772-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-5772-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-5772-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T20:42:12Z","timestamp":1660509732000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-018-5772-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,21]]},"references-count":50,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2018,11]]}},"alternative-id":["5772"],"URL":"https:\/\/doi.org\/10.1007\/s11042-018-5772-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,21]]},"assertion":[{"value":"26 September 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}